Prescribed-Time Observer-Based HI-RL Secure Output Tracking Control for Heterogeneous MASs Under DoS Attacks
针对遭受混合拒绝服务攻击的异构线性多智能体系统，提出一种混合迭代强化学习算法，结合预设时间观测器实现安全输出跟踪，无需初始可行控制策略和系统动力学知识。
For unknown continuous-time heterogeneous linear multiagent systems (MASs) under mixed denial-of-service (DoS) attacks, a novel reinforcement learning (RL) algorithm named hybrid iterative (HI) is proposed in this article to solve the secure output tracking problem based on a prescribed-time observer. Considering the scenario that MASs are subjected to mixed DoS attacks that can cause the connectivity maintained or broken of the network communication topology, a distributed resilient prescribed-time observer is designed to accurately estimate the leader’s state and output within a prescribed time. Then, the secure output tracking problem of heterogeneous MASs is converted into the optimal linear quadratic tracking (LQT) problem by introducing a discounted performance function, and inhomogeneous algebraic Riccati equations (AREs) are further derived to solve it. Meanwhile, an HI-based data-driven RL algorithm independent of the initial admissible control policy and the system dynamics knowledge is proposed to learn the optimal solution of inhomogeneous AREs. Compared with the traditional RL algorithms, that is, policy iteration (PI) and value iteration (VI), HI can not only remove the restrictions of the initial admissible policy in PI but also converge to the optimal solution faster than the VI. Finally, comparative simulation verifies the effectiveness of the theoretical results.